多样性反馈控制自组织微粒群算法.pdfVIP

计算机研究与发展 ISSN N 111777TP Journal of Computer Research and Development 45 (3) : 464～471 , 2008 基于群体多样性反馈控制的自组织微粒群算法 1 ,2 2 1 介 　婧 　曾建潮 　韩崇昭 1 (西安交通大学电子与信息工程学院　西安 　710049) 2 (太原科技大学系统仿真与计算机应用研究所 　太原 　030024) (jjin g277 @sohucom) SelfOrganized Particle Swarm Optimization Based on Feedback Control of Diver sity Jie Jing1 ,2 , Zeng Jianchao2 , and Han Chongzhao1 1 ( ) School of Elect ronic and Inf orm ation Engineering , Xi ’an Jiaotong U niversity , Xi ’an 710049 2 ( ) Division of System Sim ulation Computer A pplication , Taiyuan U niversity of Science Technology , Taiyuan 030024 Abstract 　Particle swarm optimization ( PSO) is a novel swarm intelligence algorithm inspired by certain social behavior of bird flocking originally Since proposed in 1995 , the algorithm proved to be a valid opti mization technique and has been applied in many areas successfully However , like others evolutionary algo rithms , PSO also suffers from the premature convergence problem , especially for the large scale and com plex problems In order to alleviate the premature convergence problem , the paper develops a selforganized PSO ( SOPSO) SOPSO regards the swarm as a selforganized system , and introduces negative feedback mechanism to imitate the information interaction between the particles and the swarm background Consid ering swarm diversity is a key factor influencing the global performances of PSO , SOPSO adopts swarm di versit y as main dynamic information to control the tuning of parameters through